Lantern

Like pgvector, it enables storing vector embeddings alongside relational data in Postgres and querying them with SQLbut Lantern focuses on delivering faster index build times and query throughput than pgvector for large-scale deployments where those performance characteristics matter.

The extension implements HNSW indexing with performance optimizations targeting production-scale embedding stores: parallel index construction that utilizes multiple CPU cores, support for external index building and importing, and quantization options that reduce memory requirements for large embedding collections.

Lantern uses the usearch library for its core ANN implementation, benefiting from that library's performance engineering. It supports Euclidean, cosine, and inner product distance metrics, covering the similarity functions used by the major embedding model families.

Engineering teams that have standardized on PostgreSQL and need vector search performance beyond what pgvector provides, companies building semantic search or RAG applications who want to avoid operating a dedicated vector database, and teams evaluating Postgres-native vector search alternatives use Lantern as a performance-focused option.

The PostgreSQL extension model means all standard Postgres featuresACID transactions, replication, role-based access control, and point-in-time recoveryapply to the vector data without additional infrastructure.

Lantern Cloud provides a managed version for teams who want the performance benefits without self-hosting the extension.